To enhance the accuracy of customer churn prediction, an improved Stacking ensemble learning method with Bayesian optimization(BO) incorporated was introduced. First, base learners were selected based on their predictive performance and inter-model correlations. Noticing the fact that the performance variation among base learners was neglected in the traditional Stacking methods, the Bayesian optimization was introduced to fine-tune the weights of each base learner for minimizing prediction errors. Finally, the weighted predictions from the base learners were combined, and the Logistic Regression serves as the meta-learner for the final prediction. The results demonstrate that the proposed BO-Stacking model outperforms both the single models and the traditional Stacking methods in terms of recall rate, F1-score, and AUC(area under the curve) value, which validates the effectiveness of the proposed approach. This provides a reliable reference for enterprises to develop effective customer retention strategies.

Bayesian optimization(BO)  /  Stacking algorithm  /  ensemble learning  /  customer churn prediction